The present disclosure relates generally to data-driven recommendations and, more particularly, to techniques for presenting recommended media content to a user.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Recommendation systems have become increasingly more popular in recent years. Such systems are used to make recommendations to users for a wide variety of purposes. For example, recommendation systems may be used to provide recommendations for various types of media content, such as movies, music, or books. Recommendation systems may also be used to provide assistance when a user is typing a search query or text message, looking for a restaurant or hotel, or searching for the right match on an online dating site.
Regardless of its purpose, most recommendation systems are typically intended to narrow choices to help a user quickly find the best choice that meets that user's needs. The better a recommendation system is at providing choices that please the user, the more likely that the user will continue to use the service. This benefits the user because the user perceives that they are receiving more personalized service and, thus, experience a higher satisfaction level with the service. It also benefits the service provider since a satisfied user is more likely to purchase more products and services and remain loyal to the service provider.
To provide the most personalized service to a particular user, recommendation systems typically gather a great deal of data about each particular user and use that data to provide recommendations to best fit each user's needs. Recommendation systems typically use the data to produce a list of recommendations using collaborative filtering, content based filtering, or a hybrid of those two. Collaborative filtering techniques typically build a statistical model from a user's past behavior, and possibly similar decisions made by other users, and then use that model to rank items that might be of interest to the user. Higher ranked items have the most statistical likelihood of being interesting to the user. Content based filtering, on the other hand, typically focuses on specific characteristics of one or more items that a user has selected in the past to recommend additional items that have similar properties. Hybrid systems typically combine techniques from both of these approaches to find appropriate recommendations for a user.
Of all the different types of recommendation systems, there is possibly none more personal than music recommendation system. Each person's taste in music is uniquely personal. Some people have very narrow tastes in music, and may listen to primarily only a single genre such as 50's rock or modern country. On the other hand, other people may have a very wide array of music they enjoy listening to . . . everything from classical music to hip-hop to country to alternative rock to heavy metal to Top 40. Furthermore, people listen to music for various reasons. For example, music can help wake you up, calm you down, help you exercise, motivate you, or put you in a certain mood.
Because the selection of music is such a complex and personal experience, music recommendation systems typically utilize fairly complicated algorithms that are designed to recommend music that is believed to be best suited to a particular user's taste. These algorithms may make suggestions based on genre, artist, or song similarity, acoustical analysis, as well as a user's particular activity, such as favorite songs, skipped songs, user ratings, and other songs in a user's playlist.
The goal of these music recommendation systems is to provide recommendations that will be selected by the user to enhance the user's experience. However, despite all of the time and effort that has been spent developing these algorithms to provide music recommendations, relatively little effort has been spent in developing techniques in presenting these recommendations to a user. The techniques described below better tailor the presentation of recommended music, or other media content, to a particular user to improve the user's experience and improve the likelihood that the user will select the recommended music.